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Using Linear Mixed Effects in Helicopter Logging Data

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This article applies a linear mixed-effects model (LME) to an unpublished helicopter logging productivity data set. The data were clustered in units, and the silviculture treatments varied between units, where units are cutblocks. When the covariance matrix was considered, it was found that the interunit variance was significant and the intraunit variance was heterogeneous. The significant interunit variance results in significant intraunit correlation, and this indicates the need to use LME to analyze these data. When turn time was considered, unit 4, which had the lowest level of group retention, had a conditional predicted value that was significantly less than the marginal predicted value. When turn weight was considered, unit 2, which had small patch cuts, had a conditional predicted value that was significantly less than the marginal predicted value, and unit 4 had a conditional predicted value that was significantly higher than the marginal predicted value. When productivity was considered, the conditional predicted value for unit 4 was significantly greater than the marginal predicted value, and unit 2 was significantly less than the marginal predicted value. In this data set it is interesting to note that productivity is independent of the number of logs in a turn and that turn weight was relatively insensitive to the available explanatory variables.
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Keywords: cluster; correlation; heteroscedasticity; productivity; statistical analysis

Document Type: Research Article

Publication date: 2010-08-01

More about this publication?
  • Forest Science is a peer-reviewed journal publishing fundamental and applied research that explores all aspects of natural and social sciences as they apply to the function and management of the forested ecosystems of the world. Topics include silviculture, forest management, biometrics, economics, entomology & pathology, fire & fuels management, forest ecology, genetics & tree improvement, geospatial technologies, harvesting & utilization, landscape ecology, operations research, forest policy, physiology, recreation, social sciences, soils & hydrology, and wildlife management.
    Forest Science is published bimonthly in February, April, June, August, October, and December.

    2016 Impact Factor: 1.782 (Rank 17/64 in forestry)

    Average time from submission to first decision: 62.5 days*
    June 1, 2016 to Feb. 28, 2017

    Also published by SAF:
    Journal of Forestry
    Other SAF Publications
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